[WIP][MLLIB] Rewrite CountVectorizer fitting with DataFrame APIs - #57755
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zhengruifeng wants to merge 1 commit into
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[WIP][MLLIB] Rewrite CountVectorizer fitting with DataFrame APIs#57755zhengruifeng wants to merge 1 commit into
zhengruifeng wants to merge 1 commit into
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What changes were proposed in this pull request?
This draft rewrites
CountVectorizer.fitto use DataFrame operations instead of converting the input to an RDD. It calculates term frequency withexplodeand aggregation, and calculates document frequency withexplode(array_distinct(...))only when DF filtering is requested. It also uses a native ordered limit for vocabulary selection.Why are the changes needed?
The current implementation leaves the DataFrame execution path for manual per-document maps and
reduceByKey. Keeping the computation in DataFrame operations lets Spark plan the aggregations natively and avoids document-frequency work when neither DF parameter is set.Does this PR introduce any user-facing change?
No.
How was this patch tested?
Added regression coverage that distinguishes document frequency from repeated term frequency.
build/sbt mllib/Test/compilecompleted successfully. The test suite was not run. Static checks:git diff --check, ASCII scan, and changed-line length scan.Was this patch authored or co-authored using generative AI tooling?
Generated-by: Codex (GPT-5)